J. Castro, J. Secretan, M. Georgiopoulos, R. F. DeMara, G. Anagnostopoulos, and A. Gonzalez, "Pipelining Fuzzy ARTMAP without Match-Tracking," in Proceedings of the 2004 Artificial Neural Networks in Engineering (ANNIE'04) Conference, St. Louis, Missouri, U.S.A., November 7 - 10, 2004. Abstract: Fuzzy ARTMAP (FAM) is a neural network architecture that can establish the correct mapping between real valued input patterns and their correct labels in a variety of classification problems. FAM has many desirable traits. Nevertheless, as the size of the data set grows to thousands, and hundreds of thousands datapoints, FAM's convergence time slows down considerably. In this paper, we focus on a FAM variant called no-match tracking FAM (NMT-FAM). We propose a coarse grain parallelization technique for the NMTFAM, based on a pipeline, and show that a) the parallelized algorithm is equivalent to the sequential NMTFAM, and b) the parallelization strategy achieves linear speedup in the order of p (number of processors). Experiments on the CoverType database support our results. Our work in this paper is an effort in the direction of demonstrating that FAM can, through appropriate parallelization strategies, be used to mine data from large databases.